DCE is the most useful MRI sequence for breast cancer diagnosis, but it often suffers from a high rate of false positive. To overcome this problem, we combined radiomics features from DCE, T2W, and DWI images to build a new machine learning model for differentiation of breast cancer. Our model achieved an AUC of 0.948 in an internal test cohort and 0.944 in an external test cohort, and reduced the false positive rate effectively. It was also found, first-order and texture features from ADC map made significant contributions to the model, suggesting the value ADC in breast cancer classification.
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